Continuous identification of gait phase for robotics and rehabilitation using microsensors
Résumé
Using microsensors for the robust and accurate analysis of human posture or gait is an interesting opportunity for rehabilitation and robotics applications. This paper describes a feasibility study in which the possibility of using a new type of embedded microsensors, based on the coupling of accelerometers and magnetometers, and developed by CEA/LETI is investigated. This study consists in identifying what part of the gait cycle is active by using a reconstruction of the knee joint angle by two microsensors fixed on tibia and thigh, during a steady- state sagittal walk. More than just an identification of a few gait states, this approach allows us to continuously extract the current position on the gait cycle. We compare the reconstructed knee joint angle with a stored reference taking into account uncertainties on the velocity and perturbations of the terrestrial magnetic field. To accurately identify the phase of the gait movement, we fuse different simple and complementary meth- ods: morphomathematics, cyclogram analysis, wavelet transform, qualitative analysis, crosscorrelation. These results encourage us to extend this work to explore the possibility of recognition of a larger set of human movements using more sensors and improved algorithms of signal processing.
Mots clés
gait analysis
mathematical morphology
medical robotics
patient rehabilitation
robot kinematics
wavelet transforms
accelerometer
cyclogram analysis
embedded microsensors
gait cycle
gait movement
gait phase identification
gait state
human movement
human posture
magnetometer
morphomathematics
qualitative analysis
reconstructed knee joint angle
rehabilitation
robotics
signal processing
steady-state sagittal walk
thigh
tibia
wavelet transform
Accelerometers
Couplings
Humans
Knee
Magnetic analysis
Magnetometers
Microsensors
Rehabilitation robotics
Robustness
Signal processing algorithms